OPEN Bioinformatics (Oxford, England) | 23 Jan 2015
R Richardet, JC Chappelier, M Telefont and S Hill
In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalised repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalisation and centralisation hinders the large-scale integration of brain connectivity results. In this paper, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630,216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognisers and then normalised against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity.
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